Field of the Invention
The present invention relates to an image processing technique of acquiring a lesion region from an image.
Description of the Related Art
As the radiologist's burden of interpretation of radiogram increases each year, the expectation for CAD (Computer Aided Diagnosis) soars increasingly. The grade of malignancy of a pulmonary nodule is determined based on, for example, the shape feature of the nodule, so it is important to accurately extract a nodule region in differential diagnosis (CADx) by a computer. FIG. 13 illustrates an example of pulmonary nodule images. The two left views show solid nodules, and the two right views show nodules (to be also sometimes referred to as “GGOs (Ground Glass Opacities)” hereinafter) having GGOs. Especially, it is highly probable that a nodule (“GGO”) having a ground glass opacity is a malignant lesion such as adenocarcinoma, so a demand has arisen for highly reliable region extraction processing for a GGO.
However, a nodule (“GGO”) having a ground glass opacity that is expected to be malignant at a high probability has a vague boundary, so highly accurate region extraction is difficult.
To solve this problem, non-patent literature 1 proposes a method of approximating a GGO region by anisotropic Gaussian fitting.
Also, non-patent literature 2 shows a method of experimentally obtaining the density ranges of a substantial portion and GGO region from the AUC value of an ROC curve, and performing segmentation of the GGO region and substantial portion by threshold processing.
On the other hand, according to non-patent literatures 3 and 4, an algorithm for segmentation using graph cuts is actively under study in recent years. In the case of, for example, a solid nodule, a nodule region can be accurately extracted by directly applying graph cuts to a CT image. However, in the case of a GGO, this operation is not easy because the boundary is vague.
In the method described in non-patent literature 1, to obtain a robust result, a nodule region is approximated not for each pixel but as an ellipsoid. This operation is useful in, for example, deriving a rough temporal change rate of the nodule size, while information associated with the detailed shape cannot be obtained.
In the method described in non-patent literature 2, the densities of respective regions overlap each other, so there is a limit in separation of each region (the interval between the background and the GGO region, and that between the substantial portion and the blood vessel/chest wall) by only threshold processing. Also, problems resulting from low resistance against noise and variations in imaging condition are posed.
[Non-patent Literature 1] K. Okada: Ground-Glass Nodule Characterization in High-Resolution CT Scans. In Lung Imaging and Computer Aided Diagnosis, Taylor and Francis, LLC, 2011
[Non-patent Literature 2] T. Okada, S. Iwano, T. Ishigaki, et al: Computer-aided diagnosis of lung cancer: definition and detection of ground-glass opacity type of nodules by high-resolution computed tomography. Japan Radiological Society, 27:91-99, 2009
[Non-patent Literature 3] Y. Boykov, M. P. Jolly: Interactive Graph Cuts for Optimal Boundary & Region Segmentation of Objects in N-D Images. In IEEE Int. Conf. on Computer Vision, 1:105-112, 2001
[Non-patent Literature 4] H. Ishikawa: Graph Cuts. Research Report by Information Processing Society of Japan, CVIM, 158: 193-204, 2007
[Non-patent Literature 5] M. Takagi, H. Shimoda: Image Analysis Handbook, New Edition, Tokyo University Press, Tokyo, 2004, 1260-1265
[Non-patent Literature 6] H. Kanamori, N. Murata: Commentary of Boosting and Its Increase in Robustness. The Journal of the Institute of Electronics, Information and Communication Engineers, 86, 10: 769-772, 2003
[Non-patent Literature 7] T. Narihira, A. Shimizu, H. Kobatake, et al: Boosting algorithms for segmentation of metastatic liver tumors in contrast-enhanced computed tomography. Int. J CARS 2009, 4: S318, 2009